Data Engineer

Cushman & Wakefield

Data Engineer responsible for developing data pipelines to support forecasting in commercial real estate. Collaborating with technical teams to ensure data integrity and automation in processes.

Posted 5/8/2026full-timeChicago • Colorado, Illinois, Texas • 🇺🇸 United StatesMid-LevelSenior💰 $114,750 - $135,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudPythonSQL

About the role

Key responsibilities & impact

Supports the development, optimization, and maintenance of Cushman & Wakefield’s commercial real estate (CRE) forecasting infrastructure across the Americas.
Engineering robust data pipelines, automating model workflows, and ensuring the integrity and scalability of forecasting systems.
Operate as a self-sufficient data practitioner, capable of independently delivering data solutions or working side-by-side with technology teams to ensure alignment and production readiness of QIG capabilities.
Prototype, build and maintain automated data pipelines for ingesting, transforming, and storing CRE and macroeconomic datasets used in forecasting models.
Ensure data integrity and consistency across all QIG’s inputs and outputs through rigorous validation and quality control procedures.
Create and maintain documentation of any synthetic data model architecture, data flows, and diagnostic procedures.
Develop internal documentation and process automation, and serve as expert on the integration, application and processing of internal data, 3rd party vendor data and other public data.

Requirements

What you’ll need

Bachelor’s or Master’s degree in Data Engineering, Data Science, Computer Science, Statistics, or a related technical field. Advanced degree a plus.
5-7 years of experience in data engineering or a hybrid analytical/engineering role, preferably in a forecasting or analytics/production environment. Real estate experience a plus.
Strong proficiency in Python/R, SQL, Databricks, Delta Lake and data pipeline frameworks (e.g., medallion architecture).
Experience with time series data, econometric / data science modeling workflows, and automation tools.
Familiarity with cloud platforms (e.g., Azure, AWS) and version control systems.
Demonstrated ability to operate in a collaborative, cross-functional environment, contributing both independently and alongside engineering and analytical teams to deliver data solutions.
Comfort working in iterative development settings, balancing hands-on execution with stakeholder collaboration and continuous feedback.
Strong attention to detail and commitment to data quality.
Excellent documentation, communication, and stakeholder management skills; comfortable operating as the technical translator between analytical domain experts and data engineering teams (when appropriate).
Excellent documentation and communication skills for technical audiences. Ability to participate meaningfully in engineering discussions.
Exposure to geospatial data concepts and CRE or macroeconomic datasets.
Experience working with agile/scrum delivery models in a data and analytics context.

Benefits

Comp & perks

health, vision, and dental insurance
flexible spending accounts
health savings accounts
retirement savings plans
life and disability insurance programs
paid and unpaid time away from work

ATS Keywords

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Hard Skills & Tools

PythonRSQLDatabricksDelta Lakedata pipeline frameworkstime series dataeconometric modelingautomation toolsdata quality

Soft Skills

collaborationattention to detailcommunicationstakeholder managementindependent deliverycross-functional teamworkiterative developmenttechnical translationdocumentationfeedback